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Peeling back the onion: Using latent class analysis to uncover heterogeneous responses to stated preference surveys

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  • Hammitt, James K.
  • Herrera-Araujo, Daniel

Abstract

We develop validity tests for application to stated-preference estimates of WTP to reduce mortality risk, i.e., value per statistical life (VSL), and apply these to data obtained by surveying a representative sample of French adults over the internet. These tests (WTP nearly proportional to risk reduction, insensitive to small differences in baseline risk, increasing in income, and consistent with budget constraints) are satisfied by a conventional single-regression analysis of our data. Using latent class analysis (LCA), we identify important differences between respondents in their consistency with the validity tests and control for much of this heterogeneity. Estimates of VSL from the latent class that is consistent with the validity tests are smaller than estimates from the standard analysis. We estimate mean VSL for adults of about 2 million € and for children (based on parents' WTP) of about 6 million €.

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  • Hammitt, James K. & Herrera-Araujo, Daniel, 2018. "Peeling back the onion: Using latent class analysis to uncover heterogeneous responses to stated preference surveys," Journal of Environmental Economics and Management, Elsevier, vol. 87(C), pages 165-189.
  • Handle: RePEc:eee:jeeman:v:87:y:2018:i:c:p:165-189
    DOI: 10.1016/j.jeem.2017.06.006
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    Cited by:

    1. Herrera-Araujo, Daniel & Rheinberger, Christoph M. & Hammitt, James K., 2022. "Valuing non-marginal changes in mortality and morbidity risk," Journal of Health Economics, Elsevier, vol. 84(C).
    2. Adelina Gshwandtner & Cheul Jang & Richard McManus, 2017. "Improving Drinking Quality in South Korea: A Choice Experiment," Studies in Economics 1720, School of Economics, University of Kent.
    3. James K. Hammitt & Fangli Geng & Xiaoqi Guo & Chris P. Nielsen, 2019. "Valuing mortality risk in China: Comparing stated-preference estimates from 2005 and 2016," Journal of Risk and Uncertainty, Springer, vol. 58(2), pages 167-186, June.
    4. Dickie, Mark & Adamowicz, Wiktor & Gerking, Shelby & Veronesi, Marcella, 2022. "Risk Perception, Learning, and Willingness to Pay to Reduce Heart Disease Risk," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 13(3), pages 363-382, October.
    5. Gschwandtner, Adelina & Jang, Cheul & McManus, Richard, 2018. "Improving Drinking Water Quality in South Korea: A Choice Experiment," 92nd Annual Conference, April 16-18, 2018, Warwick University, Coventry, UK 273472, Agricultural Economics Society.
    6. Hammitt, James K. & Tuncel, Tuba, 2023. "Monetary values of increasing life expectancy: sensitivity to shifts of the survival curve," TSE Working Papers 23-1416, Toulouse School of Economics (TSE).
    7. Herrera-Araujo, Daniel & Rochaix, Lise, 2020. "Does the Value per Statistical Life vary with age or baseline health? Evidence from a compensating wage study in France," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    8. Daniel Herrera Araujo & Lise Rochaix, 2020. "Does the Value per Statistical Life vary with age or baseline health? Evidence from a compensating wage study in France," Post-Print hal-03353422, HAL.
    9. Robinson, Lisa A. & Raich, William J. & Hammitt, James K. & O’Keeffe, Lucy, 2019. "Valuing Children’s Fatality Risk Reductions," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 10(2), pages 156-177, July.
    10. Daniel Herrera Araujo & Lise Rochaix, 2020. "Does the Value per Statistical Life vary with age or baseline health? Evidence from a compensating wage study in France," PSE-Ecole d'économie de Paris (Postprint) hal-03353422, HAL.
    11. Jin, Yana & Andersson, Henrik & Zhang, Shiqiu, 2020. "Do preferences to reduce health risks related to air pollution depend on illness type? Evidence from a choice experiment in Beijing, China," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).

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    More about this item

    Keywords

    Value per statistical life; Latent class analysis; Paradata; Scope sensitivity; Income elasticity; Pesticides; Children;
    All these keywords.

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • D64 - Microeconomics - - Welfare Economics - - - Altruism; Philanthropy; Intergenerational Transfers
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

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